Code with Claude London 2026: Opening Keynote — Transcript

Claude's 2026 London keynote highlights AI's evolution, developer impact, and future of Cloud Code and Claude models.

Key Takeaways

AI is making programming more accessible by reducing the distance from idea to execution.
Developers are essential in bridging the gap between AI potential and real-world application.
Cloud Code and Claude platform usage is rapidly growing, demonstrating strong developer engagement.
Innovations in AI models enable complex tasks like code generation, migration automation, and mission-critical workflows.
Future product updates will focus on model improvements, secure agent management, and enhanced developer tools.

Summary

Claude London 2026 marks the first Code with Claude event outside San Francisco, emphasizing global reach.
Claude's founder shares his early coding experiences with TI calculators and HTML, highlighting the magic of programming.
The keynote discusses the increasing complexity of programming and how AI is collapsing the gap between idea and execution.
Real-world examples include Spotify's Cloud Code migration tool and Binty's foster care system improvements.
AI model capabilities are advancing exponentially, but organizational adoption remains linear, creating a gap to close.
Developers play a crucial role in translating AI capabilities into practical tools and applications.
API usage on the Cloud platform has increased 17x, with developers spending over 20 hours weekly using Cloud Code.
Upcoming presentations cover foundation models, secure agent deployment, and new Cloud Code features like agent view and routines.
The keynote stresses the importance of developers shaping AI experiences for end users across various industries.
Lisa from the research PM team reviews Claude model evolution, highlighting milestones from Opus 3 to Opus 4 and beyond.

Full Transcript — Download SRT & Markdown

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Heat. Heat. Heat. Heat. Morning, London. Hello. This is, uh, this is the first time that we've taken Claude, uh, Code with Claude outside of San Francisco, and, uh, I'm very excited to be here with everyone. Um, before we jump in, can I take a quick

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selfie? Is that okay? All right. Ready? All right. I think we got it. I, um, I want to start today by telling you how I originally got into coding.

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I actually learned on a scientific calculator, and, uh, I found out that I could program my calculator to help me pass math tests and do well on homework.

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I was using a TI 83 calculator. I think in the UK, they use Casios, uh, Casios for this.

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And I found out that you could write little programs in TI Basic. This is like the Basic dialect for TI calculators.

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And, um, I could reference the little programs when I couldn't remember what to do.

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It worked beautifully. I got great scores on my math tests and my homework. And then I taught my classmates how to program their calculators, and they also got great scores on their math tests and their homework.

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At some point, I even published a guide on the internet for how to program scientific calculators.

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I was, uh, I was 13 when I wrote this. That was my introduction to software. It was very practical, and, um, as a kid, it just felt like magic.

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Around the same age, I figured out HTML, not to build a startup, but to make my eBay listings look better than everyone else's so I could sell Pokémon cards.

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There were tables, blinking text, the whole thing. And, uh, it worked. I sold three legendary birds for 99 cents each.

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They were all holographic, and, uh, in hindsight, I wish I would have held on to those.

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There was a whole generation of us like that, right? We didn't learn to program from a textbook. We learned from tinkering.

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And I still remember the giddy feeling that I got when one of my calculator programs ran. You made the thing, and it did what you wanted.

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And then somewhere along the way, programming got complicated. Uh, compilers, type checkers, build systems, package managers, 12 config files before you could write a single line of code. The distance between I have an idea and it runs just

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kept getting longer. What's happening now is that distance is collapsing again. You describe a problem, and the program shows up.

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It's the calculator feeling, except the calculator can write a distributed system. Now I get to watch people experience the same feeling of bringing ideas to life, and it's happening on a scale none of us imagined even a year ago.

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It's so gratifying to talk to people experiencing this every day, from individual indie devs to our biggest customers that are taking on significant challenges. For instance, Spotify uses Cloud Code to migrate thousands of repos. The team led by Nicholas

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Gustiffson built a background agent on Cloud. It reads a migration that's described in plain English, and it runs it across a fleet of agents opening PRs.

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It's now merging over a thousand PRs a month into production, and it's cutting migration time by over 90%.

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That's real engineering hours back. And sometimes speed isn't just about efficiency. It's about delivering on a mission.

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Felicia Coruru is the co-founder and CEO of Binty. Her software runs the systems that case workers use to place kids in foster care.

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The paperwork, the home visits, the licensing process. And this year, her team used the Cloud API to give case workers back hours that they used to spend on paperwork.

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And they took 20 days off the process of licensing a foster family. Twenty days.

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It's not just an efficiency metric. That's a kid connecting with a family. That excitement of solving problems is something I hear from people all the time. And I'm going to guess that everyone here experiences this differently.

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Some of you are living on the frontier. Some of you are bringing along the people around you.

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Many of you can feel the ground shifting under us, and you want a view of maybe what's coming next, and all of us at Anthropic can feel that shifting happening too. The industry is moving at incredible speed, and that makes sense when you look at

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the exponential and how fast these models are getting better. Remember a couple years ago, the frontier of model development was so good that Cloud could draft a pretty good git commit.

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A year ago, we were sitting on stage at our first ever Code with Cloud event in San Francisco. Opus 4 was the headline.

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And the idea that Cloud could run for a few minutes and build an entire feature without a human checking in, that was crazy.

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Six months ago, agents started running end to end overnight, and I started being able to wake up to finished work.

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Then, uh, last month, Mythos read the entire OpenBSD source tree, and it found a 27-year-old vulnerability that survived every human reviewer, every fuzzer, every static analyzer that was thrown at it for almost three decades.

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The jumps keep getting bigger, and the intervals keep getting shorter. But even though model capabilities are improving on an exponential, most organizations are still adopting AI on a linear path.

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That means there's a growing gap between what AI can do and what it's actually doing for people.

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Closing that gap and translating model capability into something that people can actually use is what you all as developers do. And you're doing it year over year. API volume is up nearly 17x on the Cloud platform. And on Cloud Code, the average developer is now

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spending over 20 hours a week running Cloud. Like you, we've been shipping a lot lately, and we want you to walk away today with a clear picture of where we're headed.

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So you can plan for it, and you can ride the exponential with us. This event is how is about how we're making our products work better for all of you.

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And this morning, we're going to show you what that looks like. First, Lisa is going to talk about our foundation. That's the model layer. And she'll explore more about what's going on with our frontier models and what's coming next. On the Cloud

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platform, we're shifting updates to Cloud managed agents. And Angela and Caitlyn are going to walk you through how you can securely build and deploy agents at scale. And then finally on Cloud Code, CAD and I will walk you

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through the latest features like, uh, the new agent view and then important new primitives like routines to let Cloud prompt itself even when you're away from your computer.

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All of it comes back to you and what you're going to build because most people are never going to call the Cloud API. Most people are never going to run Claude in a terminal.

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They're gonna experience AI through something one of you built on the Cloud platform. A designer exploring new directions with Canva.

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A lawyer getting a brief out the door faster with Lora. Or a developer using any one of the world's best coding agents.

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You all shape what AI feels like for everyone else. We'd never be able to build something, build everything that people need to solve their problems. This is only something that you all can do.

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So, uh, thanks for being here today. Thanks for partnering with us to shape what AI looks like for the world, and for joining us as we all reexperience those feelings of learning to code and solve problems again. Up next, Lisa from the

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research PM team. Hi. So I joined Anthropic in 2023 and have been a part of every model launch since Claude 3. That means I've been involved in bringing 17 different versions of Claude to end users and developers.

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Claude has come a long way. Opus 3 launched just over two years ago and was our first model that was proficient at writing long-form code.

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Sonnet 3.5, new Sonnet 3.6, six really was the first model that could use a computer safely.

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Sonnet 3.7 was our first model that would think before giving answers. And last year at this event, I introduced developers to Opus 4. Opus 4, we didn't know it really at the time, but it was our first model that could write complex

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Excel files and PowerPoint documents. And now with our most recent models, Opus 4.7 and Mythos preview, we'r

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So we haven't slowed down. In fact, we've been accelerating. We shipped eight Frontier models in the past 12 months.

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Each one builds on the last, helping you write better code and deliver more advanced experiences to your end users.

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Claude's behaviors and capabilities underpin everything else you're going to hear about today. As Cloud gets stronger, your starting line moves forward.

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Like Boris mentioned, we talk a lot about the exponential adanthropic. We believe as model intelligence increases the value of use cases increases exponentially.

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So consider coding agentic coding is far more impactful than code autocomplete. In this way incremental intelligence creates new markets and grows the pie.

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As we build better versions of cloud we are excited to see benchmarks improve. But what's really exciting is when Claude can do something that it couldn't do before.

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Tool use, computer use, thinking that adapts to the task at hand, agentic plans that hold agentic loops that hold a plan over hundreds of steps, long context windows.

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We pioneered these areas. So when you're building on cloud, you're building on the model family that shipped these capabilities first. and has had the longest to make them reliable.

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These capabilities are essential for code, but the foundations of model intelligence have gotten strong enough to support a diversity of tasks in a range of domain verticals.

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So Claude can iterate on designs for marketing presentations, can redline legal documents, can build financial forecasts, and even analyze genomic sequencing data.

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Let me share some examples of how customers are using our latest model, Opus 4.7.

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AMP, the coding agent, moved their smart mode to Opus 4.7 after it outperformed on their internal benchmarks.

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They also simplified their tooling since the model no longer needed as much help. Rakutin ran the model on their internal benchmark and found it resolved three times more production engineering tasks than the previous model.

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In it saw opus 4.7 catching its own logical faults during the planning phase and accelerating execution far beyond previous cloud models. We also recently launched cloud design by anthropics labs powered by Opus 4.7.

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Customers are using claw design to build production interfaces in a single sitting and we've heard that Opus 4.7 has a taste for visual design.

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Developers building on the model directly are seeing the same. We continue to hear from everyday users that Claude understands the assignment.

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Users view Claude as a valued thought partner since it's willing to challenge their beliefs and push back when they're wrong.

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So, we've made tremendous progress, but models are still imperfect. Claude absolutely still has verbal ticks, can be stumped by viral common sense questions, and sometimes does more than you asked for.

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Watching Claude improve is what makes this industry, this moment, and my job exciting. So, here's what to look forward to from here.

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Higher judgment and code taste. This means models you can trust with complex autonomous engineering.

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Context windows that feel effectively infinite as models take on longer continuous work without losing their core intent.

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and multi-agent coordination, powering teams of agents that can collaborate on goals too big for any one agent or model.

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One metric I look to to make sense to all of this change is task horizon. So how long can a model work before losing the thread?

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Last year at this time, models could reliably work for minutes. And today, most users have agents that run for hours.

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We expect future generations of Claude to run continuously. So, we will have agents that are proactive, that are always on, that know what to do without being told. These agents will be responsible for highle goals that require judgment and

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collaboration. So, let me give you some examples. Instead of asking Claude to write a project update, you would ask Claude keep the project on track this week.

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Instead of asking Claude to produce a financial forecast, Claude would own and update the forecast to keep it accurate over time.

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So what should you as developers do with all of this change? Since the exponential keeps moving, cloud models, future cloud models will be more capable than Opus 4.7 and even Mythos preview.

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You need to build for emerging capabilities, not just what works today. That means designing for the next version of Cloud, not the current one.

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We've seen countless times that the developers who win are the ones whose architecture is ready to absorb the next big jump.

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Scaffolding is what we call the parts of the agent that aren't Claude. So the loops, the instructions, the tools.

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We're seeing that as models get smarter, the scaffolding that used to help can hold Claude back.

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Claude is intelligent and resourceful and more intelligent models can al often get further with generalized primitives like a file system and sandbox computing environment.

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As models get more intelligent, you will also need to keep making harder evals and product prototypes.

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They were they are how you will notice that the exponential is moving underneath you.

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When a task that used to fail starts passing, that's your sign to ship something that you couldn't ship before.

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And finally, as the pace continues to accelerate, the teams who are getting the most out of Claude are the ones who treat model upgrades as a business opportunity.

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You should make the upgrades easy by testing by automating your evaluations and testing processes. And you should test models hands-on to better touch and feel how the improved intelligence and capabilities will help your end users.

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We are seeing the exponential continue, which means Claude will keep getting smarter and keep picking up new capabilities.

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You as developers are the first to feel that you're the first to experiment, the first to build new products, and the first to find markets that nobody else sees yet.

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Now, Caitlyn and Angela are going to show you how the Claude platform can make this reality come to life.

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So model capabilities are on this exponential, but most businesses are still on the linear. It's never been more important to take advantage of growing model intelligence to drive true business impact. But what's preventing these businesses from snapping to this

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exponential themselves? Well, it boils down to two key problems. The first one is just getting the right outcomes can be too difficult to build an agent that does exactly what you need to do. You need to do things like prompt

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optimization, tool construction, hardness engineering. There's just so much you have to do to get it just right. That's right. And the second problem is you need to ship fast, but you need to ship scalably at the same time. Everybody is moving insanely fast

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right now, and you've got to keep up. But to win, you need quality, too. It's really easy to build prototypes, but it's hard to scale in production.

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So, we've built the cloud platform to give you everything you need to ship to get the right outcomes quickly and for scale. The platform gives you API primitives that are tuned to cloud models. It gives you infrastructure to

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build and scale agentic systems and it gives you controls to operate those systems. That's right. Now, let's go back to one of the most common problems that Caitlyn and I hear all the time from businesses. Businesses need frontier level intelligence, but at

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lower costs. One of the ways that we're solving this is with the advisor strategy. So, this is really easy to implement. All you have to do is update your tools and your tools array on the messages API. And

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what we're doing behind the scenes is that we're splitting execution from advising. So, in execution, you can use a smaller model. This will make it a bit cheaper. But when that small model needs help, it can reach out to a larger model

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for advice. Yeah. So in practice, this means you can have a haiku or sonnet class model do your executing and use opus as an adviser. So we used the adviser strategy. We ran this with sonnet as an executor and opus as an

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adviser. We found that sonnet performed way better than sonnet alone. And actually we found that sonnet performed even more cheaply than on its own because opus advised it to get its work done better.

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Eve Legal, for example, used the advisor strategy and they told us they got frontier model quality at five times lower cost. And that's awesome. This is a perfect situation where you can apply it to a premium model. For example,

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you'd want to put your best foot forward with your product, but you'd have to be conscious of your costs. It's also really great in situations where you have a extremely high volume of agentic workloads. So, you have to keep an eye

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out on your ROI. All right, now let's talk about speed and scale. those two things that Caitlyn was saying that you have to do at the exact same time. Recently, we introduced Claude Manage agents. Claude manage agents is an agentic harness paired with

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production grade infrastructure. It allows teams to ship literally 10 times faster. You can now build a production grade agent in days, not months. One of our favorite examples of a customer who built on cloud managed agents was Asana.

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Asana built AI teammates so that humans can collaborate directly with agents within Asauna projects delegating tasks and things like that. And Asana wanted to build for speed and scale at the same time. So they built on cloud managed

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agents. And recently we introduced a couple of key features that were upgraded for cloud managed agents.

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Multi- aent orchestration which allows you to build fleets of agents. outcomes that allow you to specify what success looks like for Claude and Claude just iterates to get it done and dreaming where Claude can basically introspect on its previous transcripts and learn and

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self-improve. And today we're introducing two more new features. The first one is self-hosted sandboxes. So this means that you can now have Claude execute work on your own server. The other thing is MCP tunnels. And for many people who build internal agents, being

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able to access your internal MCP servers securely is now possible on Cloud Manage agents through MCP tunnels. Cloud manage agents spawn sandboxes when it's time to execute work like editing files. And you can still choose to use our sandboxes to

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do that work, but starting today you can also use your own. And we're introducing this with first class support for Daytona, Cloudflare, Verscell, and Modal Sandboxes with super easy integration.

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So, it's easy to get started with any of these providers. With MCP tunnels, you can have your MCP servers hosted behind your firewall on your private network so that Cloud Manage agents can access them without exposing them over the public

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internet. Should we check them out? Let's do it. So, Caitlyn and I have been working with a fictional company called Counter.

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Counter offers software for small businesses to be able to build up a digital storefront. Encounter really wants to become an AI native business and one of its most common workflows is that it's constantly running growth experiments to optimize its merchant

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onboarding. Kalin has graciously offered to help build a growth agent on cloud manage agents for counter.

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So here we are in counter slack workspace. I'm in here with my colleague Gabe. We're wearing our super cool counter hats and we're ready to go. So, we're really excited to see how our latest growth experiment on our

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onboarding flow has performed. And you can see that our new growthbot came into the Slack channel and it told us there's actually a really clear winner in our most recent experiment. The simpler version of the onboarding flow is way

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outperforming the older, longer version of the flow where merchants were kind of dropping off before they ever finished signing up for counter. The agent is proactively telling us it's calling the experiment and it's going to get started on cleaning up the old variant. which is

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awesome. So, let's take a look at how this agent actually is set up. And to do that, I'm going to use the cloud API CLI and the cloud developer console.

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So, first let's check out the agents configuration. You see here we've got our system prompt, our skills, our tools. Well, let's take a look at the MCP servers. First, we've got Slack.

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Makes sense, right? That bot was talking to us in Slack. Then we've got our data warehouse so that the bot can go and read data about the experiments and be able to determine what might be winning.

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We've got our feature flags MCP server so that the bot can call experiments or start new ones and and take actions like that. But let's look at these URLs. So for Slack, regular public MCP URL, but for the data warehouse and features flag

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services, you might notice something different here. We've got these URLs that are behind tunnel.anthropic.com.

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And what this means is both of these MCP servers are behind counter's firewall and only accessible via the tunnel so that counter can kind of meet those requirements of making sure that its MCP servers stay on its private network. And

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so let's pop into the cloud developer console and see how this is set up. So for counter, they set up a gateway in their own private network. Then they establish a secure connection to Anthropic. As a result, any agent that

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Caitlyn builds that uses the MCP tunnel is now able to securely access internal MCP servers.

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You can go ahead and create these MCP tunnels directly in the cloud developer console. And we give you a couple of different configuration options so you can adjust them accordingly.

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So counter's next big requirement is that agent when it goes to do things like write code and execute code, it it wants us to do those things on a self-hosted sandbox. it doesn't want to run these things on public

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infrastructure. So what we do is for our cloud manage agents, we can set up our environment to make sure that we're using a self-hosted sandbox. So with the self-hosted sandbox, now what's going to happen is when the agent actually needs

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to execute work, it's going to put a work item in a queue. And here counter is actually using Verscell's uh self-hosted sandbox. And so what will happen is that Verscell will actually pick up that work item and then they

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will actually spin up a sandbox in counter's versell account in order to execute the work. So one of the cool things about cloud manage agents is that we've got a whole bunch of really rich observability within the console

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experience. And so let's actually go check out the session that ran that resulted in that Slack thread that we saw earlier. And so you can see here that this agent is running a whole bunch of different tools. But we can briefly

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take a look and make sure that some of the things that we cared about around MCP tunnels and self-hosted sandboxing are working the way we want. So first we've got this set flag winner tool call, which means that the agent was

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able to work through the MCP tunnel to call that MCP server for the feature flag service. And then you can see here too when the agent decides it's time to write the code to clean up the old variant, it checks out a new git branch

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and it's doing this work on our Versell self-hosted sandbox. Awesome. Let's head back to Slack and take a look at what this Growthbot agent has been doing. So, first off, Growthbot is very proactive.

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Not only has it figured out that it should default the uh simplify variant, but it also put together a PR in order to actually do that defaulting and clean it up. And when that work gets executed, it's done on counter servers via the

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self-hosted sandbox. Another great thing in its productivity, you can see it also gave us a little screenshot. So you can see the old variant, which is a bit messy, and the new one, and that definitely looks cleaner.

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Not only is this agent proactive, but it's also collaborative. You can see here, Caitlyn asked, "What next?" And Growthbot was actually able to detect that another great opportunity could be to solve this 46% drop off that it found

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in the onboarding flow. Now, Growthbot was able to calculate that because it was able to query the data warehouse securely through an MCP tunnel. and we love that it ended up by asking us, do you want me to get started on that?

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Thank you very much, Claude. All right, let's wrap up. So, today we introduced two big upgrades to Cloud Manage agents with self-hosted sandboxes and MCP tunnels. With this collection of feature upgrades, it's now easier than ever to become an AI native

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business yourself and build agents for all of your workflows and ultimately accelerate whatever you are building.

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And now Kad and Boris are going to come and talk to you about how Cloud Code is making it even more fun to ship as a developer.

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Thanks. Angela and Caitlyn just showed you how the Quad platform bridges the gap between what models can do and what agents businesses ship. We see a related challenge on cloud code to bridge the gap between model capabilities and how

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every developer can benefit from them. First, I want to take some time to just thank all of the builders in the room with us today and watching online. Thank you for trusting us and your production codebase back when Sonnet 37 was our

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frontier model and back when our product was rough around the edges. Your support is what makes the team so excited to come in every day and make Cloud Code better.

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Let's back up to why Cloud Code exists. Our mission is to help every builder close the gap between having a great idea and having a shipped product. The way that we enable this is by building tools that elicit the frontier

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intelligence from our models and making these tools accessible to every builder. And we don't think of ourselves as having a finished road map to hand to you. We think of ourselves more like mountaineers just climbing alongside you in terrain

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that none of us has mapped before, figuring things out as we go. And we're growing with you with increasing AI capabilities and helping you manage these new challenges as they emerge.

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I remember looking back to last year when I would give quad code a task and I would read every single edit it tried to make every permission prompt. I would give it superdetailed guidance about what to do and what not to do. Just

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walking it through the details of the change. Now most of us are running in auto mode to delegate permissions to Claude and we're checking in after Quad has already tested, verified its changes and has a PR ready for us.

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Looking back, Quad Code started in the CLI and the terminal is still the interface for power users who want a minimal text interface and the most control and customizations.

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Then we added the IDE. This gives you the same powerful agent, but you also have the ability to follow along with all the code changes as the agent is working.

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And we heard from you that many of you are now juggling multiple cloud code instances which we've affectionately been calling multi-clotting.

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We've added two new interfaces to help you manage more agents. One that I use frequently is quad code on desktop.

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It's a full screen graphical interface. It has built-in previews, a sidebar control plane, and the ability to render images and rich outputs.

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We've built desktop to be a single view across both local sessions and your cloud sessions with visual indicators of which agents are running, which ones are blocked, and which ones need your input.

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Next is our newest surface, cloud agents view in the CLI for people who actually just prefer to stay in the terminal.

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In this view, you can also see what's running, what's waiting for you, and what's done in one glance. You can reply in line to unblock. You can jump in and out of sessions, all without losing your place.

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The VS Code IDE extension and desktop app are both built on the Cloud Agent SDK, the same one that many of you are building on as well.

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And many enterprises have now adopted quad code wallto-wall anthropic. We've seen that this has driven a 200% increase in the number of PRs per engineer even as our engineering org has scaled substantially.

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Together with all of you, we're redefining and discovering what the future of engineering looks like by embracing these new challenges that we're encountering and building automations powered by Quad to tackle each.

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Here's some of the feedback that we've heard from our users and what we've built with the help of everyone in this community.

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We heard from you that you want to spend less time on code review. So, we shipped a code review product that deploys a team of agents to traverse all your code changes and auxiliary files to catch critical bugs. Thousands of companies

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use this every day, including every internal anthropic team. We heard from you that you want to code on the go. So, we launched remote control and quad code in iOS and Android so that you can fire off a task no

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matter where you are. You're no longer needing to walk around with this open laptop or you're no longer stuck at your desk. You can now go to a park, touch grass, and still get your tasks done.

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We heard from you that you're also spending a lot of time babysitting your PRs, fixing flaky CI tests, addressing code review comments, and resolving merge conflicts.

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So, we added autofix. It listens for these events and proactively fixes each one so that your PR is always green.

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And we heard from you that you want to run cloud code on new tickets and on new customer bug reports that are coming in.

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So we built routines. Configure once and cloud code can run on a schedule or in response to a web hook or API request.

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The work that used to require you to manually kick off now just happens on a schedule.

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And last, we heard from you that you're landing so much code that security teams are struggling to keep up. So, we built Cloud Security. It scans your codebase overnight, flags vulnerabilities ranked by severity, and lets you kick off cloud

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code to address each one. Each of these primitives that we've built composes together so that we can together easily adapt to the future of engineering.

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Everything I've covered is something an individual can pick up today, but it's especially exciting to see how a range of companies have adopted cloud code at the scale of their entire engineering orgs.

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First, Shopify powers e-commerce for millions of merchants worldwide and has built AI use into its entire engineering culture.

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They use quad code across the entire company, both across the engineering org, but also non-engineers, so product managers, designers, data scientists, and they're bringing it directly into their platform to stand up tools at scale.

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Andrew McNamera is the director of applied AI at Shopify and in his words, the speed is just crazy and quad code has transformed how they build their internal tools.

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Another example is Marcato Libre. They're Latin America's most popular e-commerce site with over 100 million buyers.

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They have a team of 23,000 engineers and their org runs on cloud code. When that happens across an org, the work itself changes. Engineers are pointing agents at debt, tech debt that people haven't had the time to fix themselves. It's

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reviewed more than 500,000 PRs with human oversight and modernized more than 9,000 of their apps.

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Oscar Mowen leads technology and is aiming for 90% autonomous coding in a fully agent-driven PR loop by Q3 of this year.

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But the detail I love the most isn't just the number. It's that managers and VPs who haven't committed code in years are now shipping again.

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Quad code is putting coding back in the hands of people who've spent the last decade in reviews and roadmap sessions instead of in their codebase.

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And we see this across the entire industry. Millions of developers are getting more products shipped at higher quality and at faster speeds.

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Now let's see what this actually looks like in practice. To take you through it, please welcome the creator of Quad Code, Boris Churnney.

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I just realized we're matching. Thanks, Cat. Um, I'm going to jump into the demo in a sec, but um, first I just want to say everything that we're showing still feels magical to me.

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using cloud code, I still get those same feelings that I got when I programmed my first calculator. And even inside Anthropic in Slack, all day we're trading screenshots of things that engineers are doing in the wild with Cloud Code. It's it's just so exciting

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to see. Um, and so today I'm excited to share some examples of Cloud Code's magic with uh with all of you. For this demo, let's imagine that we're an engineer at Acme Pay. It's a payment infrastructure company. We're gonna start in the Cloud

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Code desktop app. And we're gonna start by working on just one single task. And in this session, Claude is working on adding refunds to Acme's merchant dashboard. It's working on a full implementation. Item potency so a duplicate web hook doesn't double refund

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a merchant. multicurrency handling across all the regions that Acme serves and uh audit logging for the compliance team.

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It writes the implementation and it's going to verify its own work. Quad pulls up the merchant dashboard in the browser, triggers a refund, and the modal closes before the success toast appears.

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It's a real edge case. Claude sees the failure. It traces it back to a race condition in the optimistic update. It fixes it and it verifies it actually works in the browser before calling the task done.

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Now let's zoom out. Cloud code isn't running just one session here. It's one of many sessions all running and being managed in parallel.

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In the cloud desktop app, you can now see all your cloud code sessions. Which ones are running, which need your input, which have PRs that have already been merged and closed.

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Synchronous coding is now just a slice of what's happening at any given moment. And we think that going forward more code is going to be written in an async way. And this is why we keep talking about verification. If cla can check its

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work, you can just let it run while you work on something else and you can come back to a fully working result.

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A lot of my code these days is written by routines. I'm not the one doing the prompting. I'm the one that creates a routine that does the prompting.

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For the engineers in the room, think of it like a higher order function. Routines are a higher order prompt. For example, let's go back to the refund session we just looked at. A teammate found a GitHub issue overnight. A

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routine watching the repo picked it up async and then it kicked off the working clock.

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With routines, developers can set up async automations and wake up to PRs that are ready to merge. Here's our routines view.

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Routines can be run on a schedule. They can get kicked off by web hooks or even kicked off by arbitrary API calls. And you can run them locally on your machine or on remote cloud compute.

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Let's look at one more feature in desktop. This is CI autofix. What it does is it watches the PR that the last session just opened and its job is to babysit the PR to production. It's going to autofix any comments from code

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review and security review, autofix CI, auto rebase if there's any merge conflicts. And look at what just happened. CI flicked on a network timeout. The routine woke up, diagnosed it as a known infra issue, and retried the job.

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And actually in the cloud code code base we we tell it to fix the root cause instead, not just retry.

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And CI is green. The engineer that owns the PR is never going to see the red X and that work is off their plate.

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That's the shift. The default isn't I'm going to prompt Claude code. The default is now I'm going to have Claude prompt Cloud Code.

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Everything you just saw is available today, uh, including routines and all the latest updates to the Cloud Desktop app. We're excited for you to try them out and let us know what you think. We hope these features continue to let you

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close the gap between your ideas and shipping products. And that's really what every talk today was pointing at. Lisa's capability curve, Angela and Caitlyn's agents that run on infrastructure that you control.

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What Cat and I just showed you. These are three layers of one story. The capability is already here and the remaining gap is how fast we put it to work.

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I encourage you to spend the rest of today exploring these layers, research talks if you're evaluating the models, uh cloud platform sessions if you're building for your users, and cloud code workshops if you want to learn more ways to bring cloud into your

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day-to-day development workflow. Dive in, go deep, and start building with us. Thank you.

Topics:ClaudeCloud CodeAI programmingdeveloper toolsmachine learningsoftware automationAI modelsCode with ClaudeAnthropicAI adoption

Frequently Asked Questions

What is the main focus of the Code with Claude London 2026 keynote?

The keynote focuses on the evolution of AI programming, developer impact, and upcoming features in the Cloud Code and Claude platforms.

How is AI changing the programming experience according to the keynote?

AI is collapsing the gap between having an idea and running code, making programming feel more magical and accessible again.

What are some real-world applications of Cloud Code mentioned in the keynote?

Spotify uses Cloud Code to automate repo migrations, and Binty's software helps case workers reduce foster care licensing time by 20 days.

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